{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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geOCOmhfQbJzcgipj6tSpLFiwgM7OTqZMmUJnZycLFixg6tSpRRfNbHPtCdxf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\nkgOUmZk1pP8PLq606jad/kEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1046b5748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load Plot.py\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib \n",
    "%matplotlib inline\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(1, 2)\n",
    "#plt.suptitle('OPLS-AA/M in NAMD vs GMX')\n",
    "df = pd.read_csv('ANALYSIS_ZZZ.csv',delim_whitespace=True)\n",
    "df[['RES','BOND','ANGLE','DIHEDRAL','IMPROPER','VDWL']].plot(kind='box',ax=ax1,rot=45)\n",
    "ax1.set_ylabel('GMX - NAMD')\n",
    "df[['RES','ELEC','TOTAL']].plot(kind='box',ax=ax2,rot=41)\n",
    "ax2.set_ylabel('GMX - NAMD')\n",
    "#plt.show()\n",
    "plt.tight_layout()\n",
    "plt.savefig('RES_ZZZ.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RES</th>\n",
       "      <th>BOND</th>\n",
       "      <th>ANGLE</th>\n",
       "      <th>DIHEDRAL</th>\n",
       "      <th>IMPROPER</th>\n",
       "      <th>VDWL</th>\n",
       "      <th>ELEC</th>\n",
       "      <th>TOTAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HP</td>\n",
       "      <td>0.0843</td>\n",
       "      <td>-0.00797</td>\n",
       "      <td>-0.00323</td>\n",
       "      <td>0.00623</td>\n",
       "      <td>-0.02116</td>\n",
       "      <td>1.37902</td>\n",
       "      <td>1.4372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  RES    BOND    ANGLE  DIHEDRAL  IMPROPER     VDWL     ELEC   TOTAL\n",
       "8  HP  0.0843 -0.00797  -0.00323   0.00623 -0.02116  1.37902  1.4372"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(['BOND']).tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RES</th>\n",
       "      <th>BOND</th>\n",
       "      <th>ANGLE</th>\n",
       "      <th>DIHEDRAL</th>\n",
       "      <th>IMPROPER</th>\n",
       "      <th>VDWL</th>\n",
       "      <th>ELEC</th>\n",
       "      <th>TOTAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>I</td>\n",
       "      <td>-0.03197</td>\n",
       "      <td>0.04611</td>\n",
       "      <td>0.01671</td>\n",
       "      <td>0.00193</td>\n",
       "      <td>-0.00596</td>\n",
       "      <td>-0.68315</td>\n",
       "      <td>-0.65624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  RES     BOND    ANGLE  DIHEDRAL  IMPROPER     VDWL     ELEC    TOTAL\n",
       "9   I -0.03197  0.04611   0.01671   0.00193 -0.00596 -0.68315 -0.65624"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(['ANGLE']).tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RES</th>\n",
       "      <th>BOND</th>\n",
       "      <th>ANGLE</th>\n",
       "      <th>DIHEDRAL</th>\n",
       "      <th>IMPROPER</th>\n",
       "      <th>VDWL</th>\n",
       "      <th>ELEC</th>\n",
       "      <th>TOTAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>I</td>\n",
       "      <td>-0.03197</td>\n",
       "      <td>0.04611</td>\n",
       "      <td>0.01671</td>\n",
       "      <td>0.00193</td>\n",
       "      <td>-0.00596</td>\n",
       "      <td>-0.68315</td>\n",
       "      <td>-0.65624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  RES     BOND    ANGLE  DIHEDRAL  IMPROPER     VDWL     ELEC    TOTAL\n",
       "9   I -0.03197  0.04611   0.01671   0.00193 -0.00596 -0.68315 -0.65624"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(['DIHEDRAL']).tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RES</th>\n",
       "      <th>BOND</th>\n",
       "      <th>ANGLE</th>\n",
       "      <th>DIHEDRAL</th>\n",
       "      <th>IMPROPER</th>\n",
       "      <th>VDWL</th>\n",
       "      <th>ELEC</th>\n",
       "      <th>TOTAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M</td>\n",
       "      <td>0.02584</td>\n",
       "      <td>-0.00613</td>\n",
       "      <td>0.01191</td>\n",
       "      <td>0.00849</td>\n",
       "      <td>0.01041</td>\n",
       "      <td>-0.80623</td>\n",
       "      <td>-0.75573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RES     BOND    ANGLE  DIHEDRAL  IMPROPER     VDWL     ELEC    TOTAL\n",
       "12   M  0.02584 -0.00613   0.01191   0.00849  0.01041 -0.80623 -0.75573"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(['IMPROPER']).tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RES</th>\n",
       "      <th>BOND</th>\n",
       "      <th>ANGLE</th>\n",
       "      <th>DIHEDRAL</th>\n",
       "      <th>IMPROPER</th>\n",
       "      <th>VDWL</th>\n",
       "      <th>ELEC</th>\n",
       "      <th>TOTAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Q</td>\n",
       "      <td>-0.03312</td>\n",
       "      <td>0.00527</td>\n",
       "      <td>0.00728</td>\n",
       "      <td>0.0041</td>\n",
       "      <td>0.02398</td>\n",
       "      <td>-1.00903</td>\n",
       "      <td>-1.00152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RES     BOND    ANGLE  DIHEDRAL  IMPROPER     VDWL     ELEC    TOTAL\n",
       "14   Q -0.03312  0.00527   0.00728    0.0041  0.02398 -1.00903 -1.00152"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(['VDWL']).tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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